14 research outputs found

    Characterization and formation of the pristine rhizoliths around Artemisia roots in dune soils of Tengeri Desert, NW China

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    International audience13 Rhizoliths are the products of mineralization, petrification, or fossilization around 14 and/or within plant roots. Among them, carbonate rhizoliths are the most common. 15 Pristine carbonate rhizoliths with co-existing plant root relicts in the Tengeri Desert, 16 NW China were studied, with a combination of intensive field observations and 17 laboratory methods such as microscopy, scanning electronic microscopy, energy 18 dispersive X-ray spectra, radiocarbon dating, and isotope mass spectrometer. The 19 field observations revealed that the pristine rhizoliths are only present at the sites 20 where Artemisia sphaerocephala Krasch are growing i.e. in swales among sand 21 dunes. Soil moisture of the swales is the main controlling factor of rhizoliths 2

    Non-Synergistic Changes in Migration Processes between Soil Salt and Water in the Salt Patch of the Coastal Saline Soil

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    Salt patches (SPs) with surface salt accumulation pose a serious threat to agriculture in coastal saline lands. However, the migration and distribution of soil water and salt in SPs remain unclear due to complex waterā€“salt transport dynamics. In this study, we focused on typical SPs in the Yellow River Delta region and selected center site (Site 1), transition site (Site 2), edge site (Site 3), and outer site (Site 4) with varying levels of salinization. Field sampling and the HYDRUS-1D model were employed to investigate the migration process and distribution of soil water and salt in SPs, as well as the influencing factors. The results indicated significantly higher salt contents in the central sites (Site 1 and Site 2) compared to the edge sites (Site 3 and Site 4), while no significant differences were observed in soil water content. The bottom soil exhibited greater stability in terms of water and salt content compared to the surface soil. Additionally, soil water content increased with soil depth, whereas salt content decreased from Site 1 to Site 3. Interestingly, Site 4 exhibited the opposite salt distribution pattern in the whole soil depth. We observed that SPs displayed a salt aggregation structure radiating from the center to the periphery, gradually weakening in intensity. Our correlation analysis indicated that the formation of SPs may be influenced by soil particle size distribution, precipitation, and evaporation. Specifically, fine soil structure can impede the upward transport of highly mineralized groundwater, while precipitation and evaporation directly affect the leaching and upward movement of surface soil salt, resulting in uneven salt distribution in the field and the formation of SPs. These findings provide valuable theoretical and technical insights for the prevention and improvement of saline farmlands in the Yellow River Delta

    Impact of environmental factors and biological soil crust types on soil respiration in a desert ecosystem.

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    The responses of soil respiration to environmental conditions have been studied extensively in various ecosystems. However, little is known about the impacts of temperature and moisture on soils respiration under biological soil crusts. In this study, CO2 efflux from biologically-crusted soils was measured continuously with an automated chamber system in Ningxia, northwest China, from June to October 2012. The highest soil respiration was observed in lichen-crusted soil (0.93 Ā± 0.43 Āµmol m-2 s-1) and the lowest values in algae-crusted soil (0.73 Ā± 0.31 Āµmol m-2 s-1). Over the diurnal scale, soil respiration was highest in the morning whereas soil temperature was highest in the midday, which resulted in diurnal hysteresis between the two variables. In addition, the lag time between soil respiration and soil temperature was negatively correlated with the soil volumetric water content and was reduced as soil water content increased. Over the seasonal scale, daily mean nighttime soil respiration was positively correlated with soil temperature when moisture exceeded 0.075 and 0.085 m3 m-3 in lichen- and moss-crusted soil, respectively. However, moisture did not affect on soil respiration in algae-crusted soil during the study period. Daily mean nighttime soil respiration normalized by soil temperature increased with water content in lichen- and moss-crusted soil. Our results indicated that different types of biological soil crusts could affect response of soil respiration to environmental factors. There is a need to consider the spatial distribution of different types of biological soil crusts and their relative contributions to the total C budgets at the ecosystem or landscape level

    Parameters and statistics for the analysis of the dependence of daily mean nighttime <i>R</i><sub>s</sub> (Āµmol m<sup>āˆ’2</sup> s<sup>āˆ’1</sup>) on daily mean nighttime <i>T<sub>s</sub></i> (Ā°C) at 5-cm depth when daily mean nighttime <i>VWC</i> (m<sup>3</sup> m<sup>āˆ’3</sup>) was above and below 0.075 m<sup>3</sup> m<sup>āˆ’3</sup> in algae-and moss-crusted soil, and 0.085 m<sup>3</sup> m<sup>āˆ’3</sup> in lichen-crusted soil.

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    <p><i>Q</i><sub>10</sub>: <i>R<sub>s</sub></i>ā€Š=ā€Š<i>ab</i><sup>(<i>T</i></sup><i><sub>s</sub></i><sup>-10)/10</sup>; Arrhenius: <i>R<sub>s</sub></i>ā€Š=ā€Š<i>a</i>exp(<i>b</i>/283.15 8.314)(1-283.15/<i>T<sub>s</sub></i>); Quadratic: <i>R<sub>s</sub></i>ā€Š=ā€Š<i>a</i>Ā·<i>T<sub>s</sub></i><sup>2</sup>+<i>b</i>Ā·<i>T<sub>s</sub></i>+c; Logistic: <i>R<sub>s</sub></i>ā€Š=ā€Š<i>a</i>/(1+exp(<i>b</i>(<i>c</i>-<i>T<sub>s</sub></i>); <i>Q</i><sub>10</sub>: relative increase in <i>R<sub>s</sub></i> for a 10Ā°C increase in <i>T<sub>s</sub></i>; <i>Adj.R</i><sup>2</sup> is the adjusted coefficient of determination; RMSE is the root-mean-square error; <i>a</i>, <i>b</i>, and <i>c</i> are fitted parameters; values in bold indicate best fits according to <i>Adj</i>.<i>R<sup>2</sup></i> and RMSE.</p

    Parameters, statistics, and predicted values from temperature-only and bivariate models of soil respiration on the basis of daily mean values.

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    <p><i>Q</i><sub>10</sub>-power: <i>R<sub>s</sub></i>ā€Š=ā€Š<i>a</i>Ā·<i>b</i><sup>((<i>T</i></sup><i><sub>s</sub></i><sup>-10)/10)</sup><i>VWC<sup>c</sup></i>. <i>Q</i><sub>10</sub>-linear: <i>R<sub>s</sub></i>ā€Š=ā€Š <i>a</i>Ā·<i>b</i><sup>((<i>T</i></sup><i><sub>s</sub></i><sup>-10)/10)</sup> (<i>c</i><i>VWC</i>+<i>d</i>). <i>Q</i><sub>10</sub>-hyperbolic: <i>R<sub>s</sub></i>ā€Š=ā€Š<i>a</i><sup>((<i>T</i></sup><i><sub>s</sub></i><sup>-10)/10)</sup>Ā·(<i>b</i>+<i>c</i>Ā·<i>VWC</i>+<i>d</i>/<i>VWC</i>); <i>Adj.R<sup>2</sup></i> is the adjusted coefficient of determination; RMSE is the root-mean-square error; <i>a</i>, <i>b</i>, and <i>c</i> are fitted parameters; values in bold indicate best fits according to <i>Adj</i>.<i>R<sup>2</sup></i> and RMSE.</p
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